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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Differential gene expression analysis and network construction of recurrent cardiovascular events
Jiangquan Liao1, Zhong Chen1, Qinghong He1
1Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, P.R. China.
Abstract:
Recurrent cardiovascular events are vital to the prevention and treatment strategies in patients who have experienced primary cardiovascular events. However, the susceptibility of recurrent cardiovascular events varies among patients. Personalized treatment and prognosis prediction are urged. Microarray profiling of samples from patients with acute myocardial infarction (AMI), with or without recurrent cardiovascular events, were obtained from the Gene Expression Omnibus database. Bioinformatics analysis, including Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were used to identify genes and pathways specifically associated with recurrent cardiovascular events. A protein-protein interaction (PPI) network was constructed and visualized. A total of 1,329 genes were differentially expressed in the two group samples. Among them, 1,023 differentially expressed genes (DEGs; 76.98%) were upregulated in the recurrent cardiovascular events group and 306 DEGs (23.02%) were downregulated. Significantly enriched GO terms for molecular functions were nucleotide binding and nucleic acid binding, for biological processes were signal transduction and regulation of transcription (DNA-dependent), and for cellular component were cytoplasm and nucleus. The most significant pathway in our KEGG analysis was Pathways in cancer (P=0.000336681), and regulation of actin cytoskeleton was also significantly enriched (P=0.00165229). In the PPI network, the significant hub nodes were GNG4, MAPK8, PIK3R2, EP300, CREB1 and PIK3CB. The present study demonstrated the underlying molecular differences between patients with AMI, with and without recurrent cardiovascular events, including DEGs, their biological function, signaling pathways and key genes in the PPI network. With the use of bioinformatics and genomics these findings can be used to investigate the pathological mechanism, and improve the prevention and treatment of recurrent cardiovascular events.
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